Future Agi
Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.
Last verified:
What is Future Agi?
Future AGI is an AI agent safety and monitoring platform that detects hallucinations, evaluates agent performance, and provides real-time guardrails. It enables teams to test, guard, and monitor AI agents with comprehensive evaluation metrics, simulation scenarios, and continuous optimization.
Future Agi pricing
Pricing model: Freemium
Free tier available; paid Pro tier with transparent, scaled pricing. $10K credits + 6 months Pro for qualified startups. Enterprise options available.
Future Agi pros
- Real-time hallucination detection with guardrails that actively block problematic outputs
- Comprehensive evaluation with 20+ metrics including factuality, relevance, safety, and completeness
- Visual Agent IDE for building and testing AI agents without code
- Multi-turn conversation simulation with branching scenarios for thorough testing
- Open-source (Apache 2.0) with self-hosting support via Docker Compose
- End-to-end tracing, dashboards, and AI-powered alerting for anomalies
Future Agi cons
- Self-hosting requires managing 21+ microservices and infrastructure complexity
- Specific pricing tiers and feature breakdowns are not clearly detailed in available content
- Limited information on integration effort with existing agent systems
Frequently asked questions about Future Agi
What types of hallucinations does Future AGI detect?
The platform evaluates agents across factuality, relevance, safety, and completeness metrics. It includes Sentry-style error tracking and real-time guardrails to catch and block problematic outputs.
Can I self-host Future AGI?
Yes. The platform is open-source (Apache 2.0) and can be deployed on your own infrastructure using Docker Compose. Full documentation and requirements are provided.
What is included in the Agent IDE?
The IDE allows you to build and test AI agents visually, manage evaluation datasets, run structured experiments across models and prompts, and track performance with dashboards.